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Related Experiment Video

Updated: Jan 20, 2026

Design Example: Setting a Curve Using Design Data

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Published on: March 26, 2025

Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff.

Jagoda Walny, Christian Frisson, Mieka West

IEEE Transactions on Visualization and Computer Graphics

|September 4, 2019

Summary

This summary is machine-generated.

Designing complex data visualizations requires seamless collaboration between designers and developers. Current tools fail to address unique data-specific challenges, hindering effective communication and leading to design flaws in data visualization projects.

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Published on: March 10, 2021

Area of Science:

Data Visualization
Human-Computer Interaction
Software Engineering

Background:

Data visualization design projects often involve multidisciplinary teams, including designers and developers.
Existing tools lack support for the unique challenges of data visualization collaboration, unlike general interaction design tools.

Purpose of the Study:

To identify and analyze the gaps between data characterization, visualization design, and development platforms.
To highlight data-specific challenges encountered by designer-developer teams in creating new data visualizations.
To propose opportunities for future tools to enhance collaborative data visualization design.

Main Methods:

Observations and reflections from five large, multidisciplinary visualization design projects were analyzed.
Six key data-specific challenges in visualization design specification and handoff were identified.

Main Results:

Significant gaps exist between tools for data characterization, visualization design, and development platforms.
Designer-developer teams face challenges in handling data variability, edge cases, technical constraints, and communicating data mappings.
Specific challenges include adapting to changing data, anticipating data edge cases, understanding technical limitations, articulating data-dependent interactions, communicating data mappings, and maintaining mapping integrity.

Conclusions:

Current tools inadequately support the collaborative process for data visualization design.
Addressing data-specific challenges requires new tools for prototyping, testing, and communication.
Future tool development can improve the success and collaboration in data visualization design projects.